PS-GO parametric protein search engine.

With the explosive growth of protein-related data, we are confronted with a critical scientific inquiry: How can we effectively retrieve, compare, and profoundly comprehend these protein structures to maximize the utilization of such data resources? PS-GO, a parametric protein search engine, has been specifically designed and developed to maximize the utilization of the rapidly growing volume of protein-related data. This innovative tool addresses the critical need for effective retrieval, comparison, and deep understanding of protein structures. By integrating computational biology, bioinformatics, and data science, PS-GO is capable of managing large-scale data and accurately predicting and comparing protein structures and functions. The engine is built upon the concept of parametric protein design, a computer-aided method that adjusts and optimizes protein structures and sequences to achieve desired biological functions and structural stability. PS-GO utilizes key parameters such as amino acid sequence, side chain angle, and solvent accessibility, which have a significant influence on protein structure and function. Additionally, PS-GO leverages computable parameters, derived computationally, which are crucial for understanding and predicting protein behavior. The development of PS-GO underscores the potential of parametric protein design in a variety of applications, including enhancing enzyme activity, improving antibody affinity, and designing novel functional proteins. This advancement not only provides a robust theoretical foundation for the field of protein engineering and biotechnology but also offers practical guidelines for future progress in this domain.

PROFASA-a web-based protein fragment and structure analysis workstation.

Introduction: In the field of bioinformatics and computational biology, protein structure modelling and analysis is a crucial aspect. However, most existing tools require a high degree of technical expertise and lack a user-friendly interface. To address this problem, we developed a protein workstation called PROFASA. Methods: PROFASA is an innovative protein workstation that combines state-of-the-art protein structure visualisation techniques with cutting-edge tools and algorithms for protein analysis. Our goal is to provide users with a comprehensive platform for all protein sequence and structure analyses. PROFASA is designed with the idea of simplifying complex protein analysis workflows into one-click operations, while providing powerful customisation options to meet the needs of professional users. Results: PROFASA provides a one-stop solution that enables users to perform protein structure evaluation, parametric analysis and protein visualisation. Users can use I-TASSER or AlphaFold2 to construct protein models with one click, generate new protein sequences, models, and calculate protein parameters. In addition, PROFASA offers features such as real-time collaboration, note sharing, and shared projects, making it an ideal tool for researchers and teaching professionals. Discussion: PROFASA's innovation lies in its user-friendly interface and one-stop solution. It not only lowers the barrier to entry for protein computation, analysis and visualisation tools, but also opens up new possibilities for protein research and education. We expect PROFASA to advance the study of protein design and engineering and open up new research areas.

Pepblock Builder VR - An Open-Source Tool for Gaming-Based Bio-Edutainment in Interactive Protein Design.

Proteins mediate and perform various fundamental functions of life. This versatility of protein function is an attribute of its 3D structure. In recent years, our understanding of protein 3D structure has been complemented with advances in computational and mathematical tools for protein modelling and protein design. 3D molecular visualisation is an essential part in every protein design and protein modelling workflow. Over the years, stand-alone and web-based molecular visualisation tools have been used to emulate three-dimensional view on computers. The advent of virtual reality provided the scope for immersive control of molecular visualisation. While these technologies have significantly improved our insights into protein modelling, designing new proteins with a defined function remains a complicated process. Current tools to design proteins lack user-interactivity and demand high computational skills. In this work, we present the Pepblock Builder VR, a gaming-based molecular visualisation tool for bio-edutainment and understanding protein design. Simulating the concepts of protein design and incorporating gaming principles into molecular visualisation promotes effective game-based learning. Unlike traditional sequence-based protein design and fragment-based stitching, the Pepblock Builder VR provides a building block style environment for complex structure building. This provides users a unique visual structure building experience. Furthermore, the inclusion of virtual reality to the Pepblock Builder VR brings immersive learning and provides users with "being there" experience in protein visualisation. The Pepblock Builder VR works both as a stand-alone and VR-based application, and with a gamified user interface, the Pepblock Builder VR aims to expand the horizons of scientific data generation to the masses.

Arming Yourself for The In Silico Protein Design Revolution.

Proteins mediate many essential processes of life to a degree of functional precision unmatched by any synthetic device. While engineered proteins are currently used in biotech, food, biomedicine, and material technology-based industries, the true potential of proteins is practically untapped. The emerging field of in silico protein design is predicted to provide the next quantum leap in the biotech industry. Having predictive control over protein function and the ability to redefine these functions have driven the field of protein engineering into an era of unprecedented development. This article provides a holistic analysis of protein design R&D (current state-of-the-art tools and knowhow) and commercial landscape, as well as a one-stop-shop profile of in silico protein design technology for biotechnology stakeholders.

Function2Form Bridge-Toward synthetic protein holistic performance prediction.

Protein engineering and synthetic biology stand to benefit immensely from recent advances in silico tools for structural and functional analyses of proteins. In the context of designing novel proteins, current in silico tools inform the user on individual parameters of a query protein, with output scores/metrics unique to each parameter. In reality, proteins feature multiple "parts"/functions and modification of a protein aimed at altering a given part, typically has collateral impact on other protein parts. A system for prediction of the combined effect of design parameters on the overall performance of the final protein does not exist. Function2Form Bridge (F2F-Bridge) attempts to address this by combining the scores of different design parameters pertaining to the protein being analyzed into a single easily interpreted output describing overall performance. The strategy comprises of (a) a mathematical strategy combining data from a myriad of in silico tools into an OP-score (a singular score informing on a user-defined overall performance) and (b) the F2F Plot, a graphical means of informing the wetlab biologist holistically on designed construct suitability in the context of multiple parameters, highlighting scope for improvement. F2F predictive output was compared with wetlab data from a range of synthetic proteins designed, built, and tested for this study. Statistical/machine learning approaches for predicting overall performance, for use alongside the F2F plot, were also examined. Comparisons between wetlab performance and F2F predictions demonstrated close and reliable correlations. This user-friendly strategy represents a pivotal enabler in increasing the accessibility of synthetic protein building and de novo protein design.

ODX: A Fitness Tracker-Based Device for Continuous Bacterial Growth Monitoring.

Continuous monitoring of bacterial growth in aqueous media is a crucial process in academic research as well as in the biotechnology industry. Bacterial growth is usually monitored by measuring the optical density of bacteria in liquid media, using benchtop spectrophotometers. Due to the large form factor of the existing spectrophotometers, they cannot be used for live monitoring of the bacteria inside bacterial incubation chambers. Additionally, the use of benchtop spectrometers for continuous monitoring requires multiple samplings and is labor intensive. To overcome these challenges, we have developed an optical density measuring device (ODX) by modifying a generic fitness tracker. The resulting ODX device is an ultraportable and low-cost device that can be used inside bacterial incubators for real-time monitoring even while shaking is in progress. We evaluated the performance of ODX with different bacterial types and growth conditions and compared it with a commercial benchtop spectrophotometer. In all cases, ODX showed comparable performance to that of the standard benchtop spectrophotometer. Finally, we demonstrate a simple and useful smartphone application whereby the user is notified when the bacterial concentration reaches the targeted value. Due to its potential for automation and mass production, we believe that the ODX has a wide range of applications in biotechnology research and industry.